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Ep18. User Research with AI and UX for Public Services with Mike Green

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Mike Green is a user research leader specialising in research and service design for public services. He’s also a host of the Understanding Users podcast.

In this episode, we delve into the current state of user research with AI: tools, limits and opportunities. We also cover Mike’s rich background in government public services and what private companies, UX designers, and researchers can learn from it. We also discuss the state of the general UX job market and share advice for newcomers and existing people struggling to find the next job.

Don’t forget to share this episode with your friends and colleagues—it’s a conversation worth spreading!

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Also available on all major podcast platforms and Youtube.


Vy: [00:00:00] Mhm. Hey friends, welcome back. And today I have a special guest, Mike Green, who’s a UX research leader and a host of understanding users podcast. Make sure to check that out. But in this session, we’re going to deep dive into the market intricacies. We’re going to talk about the government standards, also what any other industry could learn from it.

We’re also going to deep dive into UX research. and AI. We’re going to talk about the tools Mike uses and uses it to save a lot of time and its limitations, the potential, everything in between. Obviously I’m skeptical. I have my own views, but that’s where the magic happens because we talk about the differences, but also similarities and what you can expect, I guess, as a practitioner, but also maybe someone who’s going to get into the field.

So I hope you enjoy this podcast. If so, make sure to share it with a friend, check out Mike’s podcast, and on that note, have Do enjoy. 

Mike: The classic is, um, I saw the other day, I think it was doing the rounds on LinkedIn. [00:01:00] Someone had taken a photograph in a pharmacy of a toothbrush. It doesn’t matter what brand it was, but it was, you know, now with AI.

And you’re kind of like, you know, WTF, how, you know, when AI has penetrated toothbrush shelves in shops, it’s just kind of ubiquitous to the point of madness. And everyone is jumping on the bandwagon where he’s got to have AI. Um, I think that’s a pretty, pretty, you know, pretty marginal use case. People are still figuring it out, right?

We’re all, it’s all so new and it’s changing so fast. I’ve been trying to leverage tools, the tools that I use, things like Miro, um, you know, with AI built in, you know, obviously co pilot. Cause I, the, the clients I work with uses the, you know, Office 365. Uh, and I’ve used, you know, I’m pretty impressed so far, you know, with a lot of caveats slapped over it.

And as long as you’re careful what you’re doing, you’re careful about personal data you’re inputting, obviously. But I’ve used it for things like creating discussion guides. I’ve done analysis with it. Uh, it’s, it’s, uh, you know, summarized documents for me. It’s created PowerPoint slides. It’s done a whole bunch of stuff.

And I can really see, you know, I’m not a techno utopian. [00:02:00] I definitely see the risks and the ethical qualms around that. In terms of speeding up workflow, making it more efficient, I think there’s a lot of scope. I’ll give you a case in point actually, I got a transcript the other day that teams had pushed out of an interview that I ran with a participant.

And just to try it out, I, I, I uploaded the transcript back in a co pilot. And I said, right, can you critique this for me? Tell me how I can improve. And it spat straight out with, you know, you did this. Well, think about this, think about this. And I agree with everything it did. And I thought, wow, that’s, that’s, that’s pretty impressive, particularly for, because, you know, I’ve been doing a lot of mentoring over the last couple of years of, of kind of early career researchers and in terms of, you know, I’ve said to them, You know, try this, uh, and it’s a really, it’s a useful, quick, you know, bite sized way of getting feedback instantaneously on ways you can improve.

Vy: And I love that example of a toothbrush, which I couldn’t help, but my mind wondered of what exactly Is it probably just additive AI? Like [00:03:00] it’s always something like additive layer, which to me, it seems it’s going to be, you know, we’re going to go through a lot of trash in terms of experiences and, and what’s valuable.

What’s not until a lot of that is distilled into something really usable. And, um, funnily on that note, I had, uh, Dr. Nick Fine, also fellow researcher on a podcast. And he was saying how. All the money for user research right now is in, in evaluative research, meaning that because of the tech, tech centricity and everything kind of focusing on tech and AI innovation, we almost playing catch up and trying to test and figure out what works and it’s almost like no place.

There is very little place for like prolonged discoveries. Um, but how do you find it? Cause I know you, I mean, for the audience, I think it’s, would be interesting to hear. Cause you are basically consulting ultimately, right? For clients. So maybe your work is different, but what, what are your thoughts? 

Mike: Yeah, [00:04:00] it’s, it’s a good question.

So most of my work V is government, uh, certainly in the last sort of eight, eight to 10 years since I’ve been contracting. So I’ve UK, so I’ve worked with lots of large UK government ministries, things like department for education, department for health. Uh, HMRC, the tax people, I’ve worked with the foreign office.

I’m currently working with an organization called UKEF, so UK Export Finance. Um, and there’s a quite a well established, um, methodology in, in government, across government for the delivery of digital products, projects, which is, which is really nice because it means that you can pick up and take kind of the learnings from one department.

Uh with you to another and as a sort of standardized process of doing it and typically government, you know civil servants in many cases or you know people in organizations Can sometimes i’ve found think, you know Our problems are unique and that we have a special use case and our domain is unique to us And that may be the case in terms of the subject matter But if you look at it at a higher level often the fundamental problems the user needs you uncover They’re kind of very similar.[00:05:00] 

So and and what’s unique about government is that Uh, as I’ve said elsewhere, you know, unlike if you buy a pair of trainers, the most obvious example, I can go to 50 different websites that will sell me trainers, uh, at various colors, speeds, you know, you name it, return policies, et cetera. But if I want to tax my car, if I want to, you know, book a coronavirus, uh, jab, uh, you know, numerous other examples, there’s only one place I can do that.

And that’s, that’s, that’s, Through the government and typically through gov. uk. So there are very specific requirements in terms of understanding what are the needs of users and building services that are not just usable by, you know, the likes of us, you know, tech, techno savvy, uh, people, uh, you know, of, you know, high level of education, but it’s everybody across them and particularly people who might have accessibility needs, people who might, um, be challenged in terms of, you know, visual impairment, physical impairments.

Uh, cognitive ability. So there’s a, I think what’s really key in government is the, you know, accessibility testing, uh, the assist, what we, what we term assisted [00:06:00] digital. So for example, if the, if, if the service goes offline, how can people do X, Y, Z, because they can’t go anywhere else. Or even if the service is not online, if people are nervous about technology, if they’re, if they’re reluctant, how do you build services, which kind of meet the needs of those users as well?

So I think there are. So in terms of example, I think government, I’ve said this before as well. I think government is very much in many cases. Not in every case, but in terms of digital livery is an exemplar of best practice for how you do it right. So there’s typically a discussion fair, sorry, discovery phase.

That’s typically eight to 12 weeks, which is, you know, budgeted for you have a relatively small team that’s able to go out and do generative research, understand the problem space, really kind of. You know, unpick what are we trying to build for here? What, what’s the, what is the issue? What are the users telling us?

How can that then inform some kind of designs? And I think I get the impression, I mean, my experience in the private sphere is more limited, but I get the impression that that’s not always the case in the private sector. Often because of budgets, because of timescales, because they don’t have the [00:07:00] luxury of, of that kind of well established, um, Research 

Vy: methodology to add to that, um, in private sector, you’re not as accountable to public or, you know, it’s not the taxpayer money it’s, it’s VC money.

Um, it’s different, like to me, it’s different accountability layers. And I worked with one, a digital service for gov. uk in the past, and I know exactly what you mean about. The diligence and standards and the assessment, um, the alpha beta and so forth, like all the different assessments you have to go through.

And, and it’s, I guess it, you know, it has checklists and tools and for everyone who maybe is not aware, it’s, it’s a, everything is also available online. Or, or most of it, you know, you might not have that behind the scenes of every digital service, but the toolkits, the standards, the checklists, the steps, you can really just Google and find it.

And it’s probably one of the best in terms of a governmental type of digital service [00:08:00] standards and, and workflows and everything in between. And finally, the other day I saw this recent publishing, which was, um, yet another toolkit or checklist or set of principles from GDS. Which was to do with a generative AI and it outlined, I guess, the ethical standards, the reasoning, the ultimately ethos of what’s, you know, where could generative AI could be applied in government or where it shouldn’t.

But have you yourself experienced that creeping into the actual Digital services. 

Mike: There is one digital service I’m aware of the first service to launch in government, which has AI built in, but it’s very carefully curated. There are very strict guardrails. It’s not using kind of open large language models.

It’s a very kind of specific use case. But so I think in terms of public facing services, we’re going to see more of that as time goes by. There have been some fairly. Dare I say at large scale cock ups, maybe not cock ups, but, [00:09:00] um, issues that have come to light because the press loves stories like this, where certain departments, ministries have launched services, which perhaps have a layer of kind of machine learning underneath them in terms of decision making, in terms of, you know, uh, people meeting certain criteria, where certain groups have been marginalized or disadvantaged perhaps because the model hasn’t been tested properly.

And I think that’s a very important consideration going forward, you know, obviously not just for government, but for any. Organizations launching services. So I think, so I did, I, I myself did an AI course through the university of Oxford late last year, which was really fascinating because it was a, it was a foundation course into kind of the history of AI.

You know, how machine learning works, what is generative AI? What are the kind of some of the sort of methodologies you use for building those models, but also critically kind of the ethics around it. So what do you need to think about who do you need to involve? How can you check whether your language model is not going, you know, once you deploy it, Into the world is not going to disadvantage people that you don’t want it to And lead to unintended [00:10:00] consequences and what’s been interesting is that that’s a course for people across the world of I mean, there’s lots Number of universities do similar courses at the moment And uh, and it’s what’s fascinating me is obviously i’ve got a ux hat on but there are lots of people on that course Who are from a wide variety of backgrounds, from marketing, from tech, from robotics, from, you know, uh, investors, you name it.

And, and the, there’s an ongoing WhatsApp chat, uh, WhatsApp channel and the amount of conversation that all every day, this stuff, because of course the field is evolving so fast and there seems to be new headlines every day. What’s Sam Altman up to today? You know, what a Google launch this week. Um, So it’s a very, I mean, that’s one of the fascinating things.

It’s such a fertile area. It really feels like this is moving so fast that with the best will in the world, you’re never going to keep up reading all the papers that are published and all the kind of news articles. So I’ve signed up to a couple of newsletters and I try and sort of read those every week just to get a digested view of what’s going on.

But it’s, it’s interesting, but there’s definite, you know, one needs to be aware of this and the limitations of it. 

Vy: Absolutely. And it’s good that you mentioned [00:11:00] that some other specialties are also, I guess, aware of ethical concerns, but to me, it’s like I have a bit of a pessimistic or a hat. I tend to wear is very, um.

It, it’s almost like everything at once happening type of hat, where we’re still going to play a lot of catch up in terms of putting the actual human needs or humanity needs, because AI is going to impact a lot of the things at the scale. It’s going to start at a typical set of small challenges where let’s say maybe you are.

I don’t know, a developer, let’s say, and you use chat GPT to, you know, find some sort of solution or fix a tiny bug. But even the reports which are coming out, coming up now are stating that half or roughly 56 or so percent. Of solutions by Chagibti are totally wrong. And that’s because, you know, it’s not considering of the context of the actual goal in mind the user might have, you know, the efficacy of soft.

So a lot of those [00:12:00] considerations, which from UX side, we love to solve for, but I also foresee it’s scaling a lot. Cause. All of that is already happening. And you know, the courses we create or we are part of is, is almost like preparing us to fix stuff instead of, um, being quite proactive if, if that makes sense and it’s maybe a bit of a nature of how the things go, because if you look at the, you know, history of the internet, um, it has been the same, but.

Now, today we actually reached this truly exponential curve of how technology is so much ahead of how we want it to be or how we need it to be as humans, um, as users. 

Mike: And I think, and I, you know, I’m sure we can talk about this in more detail, but my concern is that. People like us, people, you know, the user experience and the clue is in the, the you of UX, you know, the human in the loop designing for humans, that technology doesn’t replace that.

And that AI tools, I mean, you’re already seeing, uh, organizations, which are companies, which are [00:13:00] offering synthetic users. So you can carry out sort of synthetic research with avatars, either asking the questions you can have avatars replying to your kind of research hypotheses. And it, and it’s great and it makes good clickbait and I’m sure investors love it.

You know, to what extent does that match, uh, uh, uh, you know, the classical research methodology of an in depth interview with a variety of users to understand their proper context and have the real humans, or to go and observe them in the real world, doing whatever it is they could do, you know, whether you’re in a railway station or somebody’s home or in an office or in an airport, all of, all the places that I’ve been, you know, typically pre COVID before that put an end to kind of in person research.

As much but you know that some of that we need we don’t want to lose and I worry And the job market as well And I think that’s something we should talk about, you know The the hits on the job market over the last couple of years to what extent is that caused by ai? I’m sure it’s not just ai i’m sure it’s the economy as well.

But you know the Organizations that want to cut costs, deliver faster, you know, researchers typically can often be [00:14:00] depressingly kind of short circuited or cut out of the loop, you know, and if you’ve got companies promising AI tools that will do the same thing, to what extent does that risk us as, as, um, you know, as, as researchers and what we do and the, and the, you know, the validity of our insight.

So I think. That, and that’s still to play for, but it’s, it’s, it’s of concern, definitely. 

Vy: Yeah. I don’t think you should be concerned too, because, um, and I’m trying to almost ideate like how could it actually be useful, but to me it would be useful only in a very predictable, very well researched already, and very much so structured, structural data based.

Uh, models and information sets, like you have to have already so much information about human behaviors, attitudes, what, what do we actually do? What do we actually think? What do we actually say? And like, I can’t even think of a one case where that would be the case. Um, you could even argue, Oh, but we have e commerce data and we have [00:15:00] millions of data points on any other website.

We know the behaviors, but it doesn’t still tell you the whole story. It doesn’t tell you how the user got there. It doesn’t tell you their actual thinking. It doesn’t tell you if he chose a competitor or not, you know, it doesn’t tell you anything ultimately. So 

Mike: I, no, I, I agree with you and I’m absolutely a hundred percent in your camp, but I think we need to check our own biases.

We are user experience professionals. We come at this with a background in. User centered design. I suppose I’m trying to put myself in the position of a A, a product manager who perhaps doesn’t have, or a, you know, business decision maker who perhaps doesn’t have a huge amount of experience in, in, in the kind of work we do, and the value we can bring that is under time pressure.

That is under cost pressure to push something out the door that will please the shareholders, et cetera. And that’s the danger zone I think when you can see, you know, oh well we can just do this synthetically or we can get users, you know, you know, AI will, avatars will tell us what we, you know, the user.

We don’t need to talk to real people. ’cause that comes with time. It comes with cost, it comes with hassle. [00:16:00] That’s my concern is that in those circumstances I can see, you know, old fashioned research, if I can call it that, being at risk. I hope I’m wrong, but I also think probably, you know, the kind of Gartner Hype Cycle, we’re kind of still on the way up that Hype Cycle curve, um, and it’s, you know, as we talked about toothbrushes, they are, you know, everyone is charging into AI these days.

It’ll probably accelerate a bit more, but it’ll come a point where, you know, it’ll be overhyped and the solutions that are being delivered don’t meet the needs of those using them. And then perhaps we’ll have a bit of a swing back and you know, the hype cycle will, it’ll drop off down the, the sort of first peak, uh, and the pendulum to mix my metaphors will kind of swing back the other way.

So perhaps we’re going to see that, but I think it’ll take a while longer for that to happen as well. 

Vy: This is what I call the reckoning myself, but 

Mike: yeah, exactly 

Vy: where I know, and I’m already kind of okay in my head that we’re going to end up with a graveyard of a lot of dead AI wrappers and a lot of dead tools, which sometimes die on arrival.

Sometimes they’re just going to be. [00:17:00] You know, dysfunctional. It’s almost inevitable that we are going to make so many mistakes. And when I say we, it’s everyone in tech. And then there has to be that reckoning where we’re a lot of the people realize, okay, we have to do it right in a way. And a lot of that could be done in a lot of ways, but I want to hear your thoughts of like, what do you think?

Mike: Well, I think again, with an element of self interest in this, I think people like us need to carry on doing what we’re doing, you know, you need. A user centric approach. You need to talk to your users. You need to kind of understand, as we were saying, you know, their context. And you need to know kind of how to do that.

So I think the skills of how do you build a survey that actually answers questions that you need answered? How do you interview users? How do you then analyze, you know, well, first of all, it’s taking a step back. How do you, you know, how do you generate research hypotheses? How do you plan your research?

How do you conduct that research? How do you do analysis on that? How do you make sense of all of these insights draw out kind of, you know, what are the key themes we’re hearing? What are the pain points we’re hearing? How can we address those? I think, you know, [00:18:00] AI at its best can help, as I sort of said before, speed up a lot of that and make some of that process hopefully easier, faster, more efficient, but I think the We’re a long way from us being able to, you know, follow the research process through, you know, entirely with computers happily because you still need humans to make sense of that.

So I think it’s some of the fears are unfounded and I agree with you about the graveyard. I think, you know, the reckoning is coming, but it depends how far away it is. And I guess what the human cost in terms of jobs and organizational flux is in the meantime. And I think that’s something that’s still being worked out.

Vy: And I think it’s also probably depending on the market because UK, um, I feel like, well, everyone in the world is really playing catch up with, uh, VC money in, you know, startup culture from us and Silicon Valley, because that’s where a lot of that haste originates. Um, And that’s what I experienced myself.

I was just in touch with startup founder, very early stage. And, um, they are in this space where [00:19:00] it doesn’t really make sense. And I cannot obviously give out too much information, but they are working with civil engineering and transport specifically. They want to disrupt a few different. areas there, which is highly governed as well, for the right reason.

Um, it has a lot of processes, but also standards, but also limitations, basically, you know, everything which, which every other industry has, but it doesn’t really have the need for even the simplest of the digital solutions. You kind of have to find the angle of how to do it right. But that founder, because they’re brushing shoulders with other founders and other techies, they have this inclination of think, okay, I need AI If I don’t have AI as a keyword, I can’t really raise any VC money right now.

Um, or we have to figure out how to almost sustain and, and grow without growing because we don’t have cashflow. And, and this is almost like, you know, it’s like, how do you even advise that person or how do you help them then? Their take is [00:20:00] whatever you do or whatever you want, or whatever River Valley is, or whatever research states, we need to figure out how to add AI to it.

And it’s AI 

Mike: FOMO, isn’t it? That’s what it is. It’s AI FOMO. It’s me too. Everyone else is doing it, but I can see that. As you say, if you’re a founder, you know, scrambling around pitching for investor money, if your solution doesn’t somehow kind of. Follow the herd, if I can put it like that, in terms of having some kind of machine learning built in, then the chances are you’re not going to get an investment that a competitor would do.

So how successful that is, you know, to be seen, but it’s, it’s, yeah, it’s a tricky place to be in, in terms of kind of you, AI tooling. Uh, you know, you asked me before, I’m interested to know kind of what’s your experience of, of day to day use of kind of AI tooling in your work or not. Have you 

Vy: used it? It’s very hard to say.

I mean, I feel like, um, because I’m working with some AI startups, um, and I’ve been, I’ve been working with AI, but not AI, as you know, today, um, not generative [00:21:00] AI, uh, but machine learning based solutions. So very big tech, deep tech, uh, from. 2016, 2017, um, and I used to consult if a lot of different household names and global companies where that precision and safety was the biggest factor in terms of how users do the things.

And that’s where it seemed like it made so much sense because there was no such thing as AI. And I, I quoted this term or this, um, The statement, which I got from one of the data scientists when I worked with them, it was in the energy sector by the way. But, um, we said, Oh, we should probably, you know, call it AI to market it well, even if it’s internal themes.

And they just laughed and scoffed and said, you mean advanced statistics, you know, because, you know, AI, it, it, it has no meaning or it didn’t have no meaning or it didn’t have that marketing stats back when, but I feel like I’m, you know, I’m, I’m a chef and I, I, or, or I feel like I’m positioned as a chef and at home, I don’t really cook [00:22:00] that fancy, if that makes sense and as such, It’s almost like position to make or advise people or consultant things, which would make AI to support the human or customer decisions in the right way.

But I can’t find a way to use the tools myself. Like, and this is kind of like a Frank type of statement where I don’t know if design or UX or UX research get the same treatment as any other industry, but I don’t feel like it’s. I don’t feel like we are anywhere close where our use cases or our needs as researchers or designers are going to, are being addressed.

You know, I use Dovetail, I use Miro, I use all the tools you probably use before. I use other repositories and sometimes I use, um, tools from Atlation as well, you know, secondary tools, which are not really for UX research, let’s say, or, or design, but I use them. Um, and. I see those AI features, but I don’t see exactly how does that add value.

It’s almost like I [00:23:00] want, I need to want it bad enough or, or, you know, there has to be a wish for me to make a habit to use them. Um, there is this chasm which you have to cross. So I haven’t found a tool which would be sufficient for that. Obviously trialed chat GPT, trialed all the other tools, trialed those plugins or additive bits in Miro and other tools.

But still, um, it’s cool. It’s snappy, but once you’re in the heat of the moment, once you’re actually in a workshop or you’re crunching a lot of data synthesizing, you have your own methods and clearly the software is not designed for those methods. My personal story, at least, but it’s also indicative of what I observed with other people researching and designing as well.

Um, and maybe it’s the nature of work too. Uh, to some extent it is because as I’m talking about it, I’m thinking about the figma. Tools or additive tools, which are basically external startups now coming up, which want to automate the [00:24:00] product design, meaning that absolutely anyone could come into Figba, um, launch this plugin and define their app and bam, it launches you 10 different designs.

And then you can update them for prompts and things of that nature, which sounds futuristic. But even then we are so far away from what the real businesses have and what we deal with. I could give that plugin to a startup founder. Who wants just to visualize their idea so that they can hire a designer or a developer, but even then, once we have a designer or developer, most people are going to have to go deep into trenches and solve those needs or researchers or so to speak, but I don’t know, like, I, I know you sounded excited in the beginning.

Um, when you mentioned, you know, office 365 and things of that nature. If I could put up the researcher hat. You know, if we could role play for a sec, how, how did you figure out to try it? Or like, what was the intention for you to actually go and explore it? [00:25:00] 

Mike: Well, I think you talk about try it. I think that’s the word I’m, I’m in an experimentation phase.

I’ve certainly not abandoned my traditional toolkit in favor of AI. And I wouldn’t suggest to anyone to do that at the moment. I just wanted to try out in terms of. You know, preparing research, running research, or kind of supervising others, doing that mentoring people, you know, these are ways that potentially.

You could speed up your workflow, make yourself more efficient, offload some of the kind of perhaps lower level stuff, if I can put it like that. But I think as you’re, you know, as you’re talking, one of the things which is coming, you know, I thinking about what I said before, it, you know, we took you to chat a little bit about hallucinations and kind of errors and stuff.

And the downside of these learning machine learning models, as we, as we all know, is, is the kind of inaccuracy and hallucination. And as a, you know, as a researcher, I need to have confidence that, for example, the insights, if I’m going to draw out, you know, if I’ve got. 10 hours of interviews. And I want to start pulling out key themes to share with my clients or my stakeholders to say, look, this [00:26:00] is, these, these are the site key areas that they use as a flagging that we need to think about.

And if that then turns into, you know, developer time, money, investment, you need to be sure that. The kind of insights that you’re sharing are accurate and true and legitimate representation of what your users have said. So I think that’s another key area that I have concerns with at the moment is that until you can be pretty sure that speeding up the process and the machine will spit out these insights and say, users say this, this, and this, you need to be sure that that’s right and accurate and a true reflection of what they said.

Otherwise you start running off in the wrong direction very quickly. You kind of, you’re wasting time and money. So I think that that’s, that’s important. And then begs the question that if you can’t trust the output of this stuff, then why use it in the first place? Cause if you then got to go back and double check everything it’s done, you’re not saving much time.

So, uh, I think there’s, there’s huge benefits, but used judiciously, if I can put it like 

Vy: that. But you still, I guess, and I get, I don’t want to. You know, anchor much, but [00:27:00] like, you almost have to want to use it in a way, uh, or like, you have to basically make an effort, right? Like, um, because the accuracy doesn’t come automatically, right?

Like for it to be accurate, which I was just pondering earlier today for any AI tool to be accurate, you. It has to shadow you in the making. You have to correct it. You have to coach it. You have to train it because the training is not automatic, especially niche fields like UX. So, 

Mike: and that’s where the, you know, the prompt engineering comes in and kind of how, how do you, how do you create the right prompt?

How can you refine the prompts? So case in point, we were doing some analysis, uh, me and a few of the, the, the, the team I was working with a few weeks back and we had a mirror board. And as I said, we had lots of, you know, quite a few hours of interviews. It was a very big prototype. There’s a lot of insight on the board and we used mirror assist, which is mirrors kind of AI tool.

And in terms of affinity diagramming, you can, you know, highlight a particular column or a particular row or a section of the board, and you can just prompt [00:28:00] it to say, right, can you. Basically look at these post it notes. This is the insight of six users. Let’s say, can you pull out the top three pain points identified by these users and bang, it’ll do that for you.

And it’ll create three post it notes, you know, one, two, three. And I thought, and it’s pretty, it’s pretty nifty. It’s pretty impressive. And by and large, it was pretty accurate. And then we could get it to do things like, right, can you, so with the same grouping, you could say, can you pull out the top or can you tell me that the key insights from these users, let’s say, and group them into positive, neutral and negative and color code them.

So can you put the positive insights in on a green card, neutral on orange and negative on red? And it would do that. So straight away, you’ve got a kind of visual feedback. It wasn’t entirely accurate, but it was fairly accurate. And that sort of thing, I think was, it was pretty impressive. I mean, there was that kind of wow moment and the sort of, this is sexy AI at work.

You know, it wasn’t revolutionary, but in terms of quickly getting to the nub of problems, uh, I thought it was quite, um, quite useful. But how do 

Vy: you, [00:29:00] how far do you feel like we are from, uh, I wouldn’t say total automation, but at least accuracy, where let’s say you as a UX researcher, you run a workshop or you dump those, um, insights on a board.

You click one, let’s say you describe it in voice and that’s it. Um, and not attached to the technology, but how far do you think we are from that? Just from your kind of expertise, because you’ve been doing it for so long as well. 

Mike: Sorry, there’s a, there’s some kind of spitfire. I live near an airfield near Duxford airfield, and there are like low flying World War II planes that go over and there’s one that’s just gone over.

So I thought I was being bombed for a minute. Sorry. Sorry, mate. Could you repeat your question? Apologies. That’s AI 

Vy: drones for you. Um, No, uh, I was just asking, 

Mike: um, 

Vy: just asking about accuracy. Like how far do you think we are from it being like this perfect experience? I guess, and when I say perfect as well, it’s probably, it means that anyone can do UX research almost, or anyone can synthesize with the help of AI.

Um, what’s your gut feel? [00:30:00] 

Mike: This plays to my point I made before where, you know, you can, there’s a risk of us being cut out of the loop by saying, well, we don’t need experts to do this because anyone, you know, BA could do this, a product manager could do this. And I think, no, you could, they can’t, I mean, yes, they’re, you know, they’re very good at what they do, but you still need people who are steeped in research methodology and understanding.

So I think there’s, you know, it’s, it’s hard, it’s an evolving field, but I, but I think you’re right. I think, uh, you know, the models are evolving and I think there’s a difference between large scale models, which can do everything, you know, chat GPT, you can get it to write you a poem, tell you a recipe. And then there’s very context specific.

AI, which I think we’re going to see more of where companies say, no, this is a, no, there’s this specific use case with limited amounts of data, limited parameters needed. That’s, you know, a much more limited model. That is probably less risky in terms of spewing out nonsense. I mean, I say that, that may not be the case.

But I wonder whether we’re going to see more of that rather than just this assumption of, you know, the model will tell you anything about [00:31:00] anything, because there, there is much greater risk of, you know, erroneous output, if I can put it like that. Does that answer your question? 

Vy: It does. It does. I think, you know, it’s also because it’s, there are so many angles to this as well, and I don’t think we, and nothing is definite as well, where I think we are continuously learning from this.

And like, to me, the biggest challenge is positioning this in the right light so that. You know, I can position myself and my ideas and my vision for like how I want to shape my skills, but also coach other people or even share ideas on this podcast, because I personally foresee that, you know, AI is definitely going to like take A way, some of the tasks we’re doing, um, in general UX and I guarantee in research too.

Yeah, um, and, and like, you don’t have to almost be a genius to understand which ones are going to go first. And that’s the most predictable, the least abstracted steps. Cause you know, your effort in Miro, uh, to. I don’t know, put [00:32:00] 100 post it notes of actual insights and then synthesize it. It still takes you to translate what actual person tells you.

So much processing goes there, but certain steps in UX. I mean, to do specifically with a prototyping visual design, you know, color theory, color psychology, things of that nature, those things are very specific and you already have so many libraries, so much information online and even designers themselves creating public.

Assets, the libraries, the design systems, which are all based on the same atomic design principles as, as, as of today, same variables, things of that nature. You don’t have to go far basically to almost imagine that this is where it’s going to start. And I think we also have to be okay with that in a way, whereas.

My personal take and again, challenge me and tell me what you think. But I feel like UX is always going to be persistent, but there might be less of [00:33:00] actual jobs to do the UX because there’s, you know, a lot of those hours we spend are simply going to be, you know, taken away. Like they’re, they’re going to be optimized basically for that.

And if you’re a business owner, why the hell not? You know, why wouldn’t you go for it? 

Mike: No, I think you’re right and I think I mean to go back to what I was saying earlier about that We were talking about the job market I think there’s a very real risk of that and even the large tech companies, you know with their huge budgets are investing so much in ai That perhaps the heat, you know, there’s certain humans that are being you know Sacrificed on the altar of that in the short term at least but um, yeah I mean, it’s an interesting one, isn’t it?

I who knows no one’s got a crystal ball for the future You know, I think I think as long as humans are in the loop, you know As long as people like us Are there and I think and I suppose as you were talking, I was thinking it’s a tool, right? And it’s a cliche to say it because we know it is But I think the danger is when it people obsess about it instead of it being a you know Like microsoft called their tool co pilot, which is what it is, right?

And that’s what it should be in my view. It doesn’t replace us But it’s a it’s a it’s [00:34:00] a kind of helpful Perhaps more junior assistant or the junior is the wrong word because it has great greater capabilities in some ways And I think if you view it like that You Then it kind of changes the dynamics slightly and that’s how it should be viewed that, you know, if, for example, if you’re a researcher, if you’re a early career researcher and, you know, you came to me and you said, you know, what should I learn?

Go back to what I said before, I still think you need to know how to, you know, design, you know, Uh, piece of research, how to, um, you know, how to choose the best method workout, what’s the best method for you, how to go and find your participants, how to, you know, basic kind of survey design interview practice, how to do, you know, basic stats, um, all of that kind of stuff, you know, have you read a bit of the academic literature?

You know, do you understand analytics at a basic level, A B testing, some of that kind of stuff. And I think AI won’t replace any of that. You still need humans to, to know what to do, which questions to ask, how to ask the questions, how to interpret the answers you’re getting. And I think that’s where, um, you know, [00:35:00] people like us will always add value.

Um, at least I, I hope he will, 

Vy: you sound very positive and confident in that, but one thing which I think I wanted to also pick your brains on is, um, a psychology, which is, you know, your background ultimately, um, and how you started with. But do, do tell, cause you know, I don’t want to guess cause I think everyone’s journey is a bit different.

But like, do you feel like that is also coming into play? Cause a lot of that aspect, it’s one of the thresholds which technology can’t easily solve for. It can’t really overcome that threshold of understanding other people. How do you think about it? 

Mike: So my background originally is in, in model languages, uh, you know, going back quite a few years now, V.

So I did a. French and German degree and lived abroad for a number of years. Uh, and then got into teaching English. Cause I didn’t want to get a quote on quite proper job for a while. Um, and then, so I lived abroad for, for a number of years in South America ultimately, and then I moved into digital publishing, sort of language learning, publishing.[00:36:00] 

This is the early days of basically, you know, we’re talking about 20 years ago now, taking textbooks, putting them online, but in a more kind of interactive dynamic manner, so kind of e learning the earlier days of e learning. And that kind of felt like, you know, it’s an interesting place to be, but it, it wasn’t, it wasn’t dynamic enough for me.

And I, um, decided to career change. And funnily enough, you know, until probably 15 years ago, I mean, I’ve got a bit of a confession. I hadn’t even heard of UX. I didn’t know what this thing was, but I think it’s one of those things that once you, once you’re aware of it and you’re aware of it as a discipline, if you’re that kind of way inclined, you start picking holes in everything, you know, the famous Norman doors, uh, you know, you start using telephones that don’t work and, and rather than.

Blame yourself. It’s like, well, why wasn’t this thing designed properly? And I think it’s that mindset. So anyway, long story short, I did, I decided to do a master’s in psychology because it struck me as a kind of good way into UX. But you didn’t 

Vy: go with like visual side, right? Sorry to interrupt as well.

But did you, because that’s more typical to start with. You see a cool app [00:37:00] and you’re like, Oh, I’m going to design for that cool app. 

Mike: I mean, I think, I think I didn’t have a strong feeling either way. I certainly didn’t do my master’s in psych thinking I want to be a UX researcher. I just thought this is a, you know, this hopefully will lead somewhere.

And that’s where I learned, as you know, as I mentioned already, kind of the basics of research design analysis. And I did my dissertation actually, I’ve got it before we started. Recorded this. I was just looking at it, it was about, bearing in mind it was written 10, 12 years ago. Now, uh, you know how social is the mobile web?

So what I wanted to do was, uh, for, for the participants who’d agreed to, to take part, basically when they were co-located, when one of them was using a mobile phone to answer informational or needs that they all had collectively, whether it be in a restaurant on the street at home. And then log that. And I actually ran a diary study and this was kind of my very first diary study.

And I got them to, to basically record on a very simple Google form each time they did that. And this, bear in mind, the, the iPhone in those days would have been only about four or five years old. You know, we’re talking long before [00:38:00] Uber, really long before X, long before a lot of this stuff came up.

Certainly chat GPT wasn’t even dreamt of then. Um, and it was fascinating, really fascinating to then kind of take the, the, the survey insights that I got and, uh, and the quality of the qualitative and the quantitative and do some basic kind of, uh, Chi squared tests and, and make sense of, you know, at a, at a basic level, the statistics around that, um, and that, you know, and then I started working in an agency and so I had experience of kind of visual, but also research, uh, and then little by little, I think just of the two research just fitted my personality more.

I enjoy it more. Uh, you know, I’m still a very much a visual thinker, but it appeals to my, you know, kind of analytical mindset that I’ve got. And it’s taken me to all sorts of, you know, fascinating places. So I’ve done research abroad with the UK government. We did a piece of work with GDS going back a few years in Southeast Asia, where the British government was working with foreign governments to understand how certain governments actually procure it overseas.

So the public procurement process [00:39:00] for digital. Uh, so we went to a number of countries and we talked to, you know, private sector suppliers of digital solutions. We talked to government officials, both at kind of regional level and national level, you know, it was a large scale, very ambitious discovery to kind of understand.

So if you’re in Indonesia, if you’re a, if you’re a civil servant in Kuala Lumpur, how do you identify what tech tools you need? How do you then go about acquiring those? How do you build them in some cases internally? Um, so from that kind of thing through to, uh, you know, emergency response during the COVID, uh, epidemic, I was working with the department for education, so we were doing kind of COVID response solutions, getting, you know, laptops and connectivity in the form of, you know, little routers or mobile data uplift out to families in, in real need.

So there was great pressure to kind of get the solution out there, but at the same time to, to test it before we did that. Fascinating 

Vy: cases. So like, imagine it like AI. I mean, it’s, it’s practically impossible, or at least with the means we have so far, like it’s, it’s that’s, that’s perfect cases of [00:40:00] where, you know, some people who work, let’s say on, again, not, not to demean or, or kind of say that it’s lesser complexity, but some people who work on, let’s say apps or very predictable environments, which is, you know, it’s, it’s one constraint interface and the user is somewhere abroad and you can then just call them on a zoom.

It’s very different than. People who are actually physically involved in whole process. And you go through a lot of those hoops and, and, and things of that nature. And that’s where it’s exactly the case where I feel like if anything, if a lot of the things are going to be automated, those things, someone still going to have to kind of figure out and tie in all the different pieces together ultimately to solve for, 

Mike: um, yeah, exactly.

And, and, you know, show me a synthetic avatar, certainly at this point in history that can replicate a. You know, a regional government budget holder in, in, in a province in Indonesia who wants to acquire some kind of timetabling tool for their [00:41:00] civil servants, you know, it’s, you’re not going to get the same insights as you would by being there in person and talking to a human, I guarantee it.

Vy: Do you feel like maybe we’re missing a lot of that psychology background right now? Because my personal take has been that a lot of UX kind of limits itself with color psychology, maybe a bit of a gestalt loss, persuasive design elements, things of that nature, but not so much with the actual, you know, popular, but also scientific psychology.

Mike: Well, I think, I guess it depends what kind of hat you’re wearing, where you’re talking about, you know, you’re a UX designer looking at visual design or whether you’re, you know, a service designer looking at an end to end. process. So I work with a lot in particularly in government with service designers.

So you’ll look at not just, you know, screens X, Y, Z, but you’re kind of like, so what, what’s the need for a user to get to this service? What are the access points for the service? What’s their context of use at those access points? What are their informational needs? How do they flow through the service and then kind of what comes next?

So it’s not just [00:42:00] clicking, you know to go to the trainers analogy by trainers and the trainers get shipped It’s like how does that whole next piece work? You know, what follow up messages do I get? What and government is not alone in doing this obviously, you know Lots of companies are doing the end to end process is very key, but it’s that’s more than just that It’s color psychology, as you say, or kind of placement of buttons.

It’s a whole, you know, different mindset about, do you understand your users properly? So the government design service, GDS, we talked about the service standard, you know, to pass the assessment at each stage, when you pass from discovery to alpha to beta, as you will know, there’s 14 points that you have to kind of meet.

Uh, and the very first one is understand your users. And again, that’s where people like you and me come in and, you know, researchers, how well do you know the people you’re building this thing for? Uh, and until you do that, you’re, you’re wasting time and money, frankly, cause you’re just coming up with, and this is where I think, you know, AI generated interfaces where you can plug into chat GPT and say, build me five, five versions of an app for, for use case X, until you really understood the users and kind of what, [00:43:00] why they need it and what their needs are.

own constraints are, you’re, you’re missing a trick and you’re potentially going down a rabbit hole. 

Vy: Like we have so much more debt with the previous years of how, I guess we solve for, you know, how we solve problems with UX, um, because when you describe GDS, let’s say, and all those assessments, I remember like today, um, this is where you get into a room, you use your walls to plaster all the different insights you basically go through.

Needs, personas, journey maps, service, blueprints, everything together. And it’s, it’s a multidisciplinary team, which it’s a very unique type of scenario. I remember doing it in person. I don’t know if we’re doing it right now remotely or digitally, but I remember it, it being, you literally are locked in, in a room going through details and trying to, not to sell, but ultimately collaborate.

And see, okay, what’s missing, what’s right, what’s not right. Things of that nature. But I feel like even that standard, it’s something which only [00:44:00] a few organizations have been doing. Probably the most biggest ones I could think of, let’s say IBM, sometimes Google, sometimes Microsoft have been doing like big assessments or big UX projects, but not many.

So I think there’s already a ceiling, a lot of debt to kind of get to that level. And now if you’re thinking about AI. And how AI could automate UX, that’s why it’s so far away because yeah. So, you know, the standards are quite low on maybe 99, you know, random number, but 99 percent of the projects and companies out there, it’s not getting any higher just because you add AI to it.

Cause it’s not, I guess, addressing the real needs, the real tools, processes, things of that nature and principles for, for the most important part, I guess. Yeah. 

Mike: Yeah, no, you’re absolutely right. And funnily enough, I did it. I was in an assessment last week. So the service I’m working on at the moment, we had our beta assessment and they are remote these days.

I’ve done both. I’ve done face to face like you, [00:45:00] and it’s a long day. So we started at 10 and I think we finished about. 330 in the end. And it’s, you know, you have the kind of upfront piece where the, the, the sort of product owner talks about the service, why we’ve built it, what, you know, what the issues are we identified.

And then the user researcher alone has an hour. So I had an hour long slot where I was fired questions at, by the kind of assessors, you know, how well do you understand your users show evidence that you’ve done this, this, and this, and it was all very friendly and collaborative, but as you say, it’s a, it deliberately sets a high barrier.

And I think. But also high barriers mean, you know, budgets because to have all those people in that virtual room for most of the day, that’s quite a big investment of time and money. And I think for smaller organizations, it’s probably not a realistic way to go. And there’s nothing wrong with that. It’s just a, it’s a different way of doing it, but I think it’s a, It’s a nice way of doing it.

And it, it, you know, guarantees a certain level of quality in any service you put out. 

Vy: I think I would argue that smaller organizations still can do it, but it’s just that you kind of split [00:46:00] those tasks and those steps in, you know, across the days, and then you have things like design sprints and things of that nature, which is a bit bastardized way of doing the same and making a lot more guesswork.

Um, minus, you know, all the different standards and things of that nature. Um, but what are your views on the market? Because market has been shattered for quite some time. I know UK, by the way, uh, from what they researched so far is very similar to US market. Um, I know the job market? Yeah, yeah. Or, or industry as a whole.

Um, what is your kind of take on it? 

Mike: So I, you know, particularly because government contracts tend to be longer and they tend to be Not more secure, but they tip that, you know, they, the, one of the nice things about government is that you can tend to be somewhere longer and you can typically, there’s not too much of a gap between contracts.

So for example, in the last eight years of contracting, I had never had more than a week off until the beginning of this year. And then I had three months off and I was, you know, and in common with lots of people, friends of mine, very senior, you know, service designers, [00:47:00] uh, delivery managers, you know, content designers.

Uh, and we see it on LinkedIn, you know, the open to work banner. So many people have got it up. Um, and people have been looking and looking and looking and it’s alarming, you know, So for the first time I was sending out lots of messages to everyone in my network And there were lots of thanks for getting in touch, but not nothing at the moment Even the recruiters were quiet and it took a while to find, you know, my current role current contract Which has never happened before and i’m not, you know, no pity required There’s lots of people in a much worse situation than me, but it is it is worrying and you kind of think Is this a temporary blip, which we all hope it is, or is it something longer term?

And I, I don’t know, I mean, no one’s got a crystal ball, but I worry that it might be something a bit more long term, a bit more structural across the organization. That, are we seeing a retreat from user experience importance and value in organizations? I don’t know, what do you think? But certainly I’m not seeing the market picking up dramatically at the moment.

Vy: I think it’s following. So, so I guess if we pull back like a good half a year [00:48:00] where it was definitely frozen, there were no leadership roles. And to me, that’s kind of indicative if there are no like a full time leadership roles in the market, that’s where you have a lot of issues because leaders tend to hire other people.

Or they’re the ones who kind of populate the contracts and fight for budgets, you know, all the procurement and other challenges. They’re the ones who are dealing with it. And that to me is what I’m always looking for. And not just selfishly so, cause you know, um, as a, I guess UX manager for so long, but that, that to me is a clear signal.

It wasn’t good, but I see it picking up the speed again now. Which to me is very helpful for any other specialty, because if you have a strong design leader or more design leadership in any organizations, you’re going to have more contracts. So it’s almost like a projective speculative take. But I also, when I talk to quite a few people on this podcast, I talk to.

Um, Hank’s who, who’s LinkedIn persona, let’s say, and Hank is basically designer turned recruiter. Uh, we talked [00:49:00] last year, late last year, but it’s still, I think, stands where I think in us, or has been massive bias for UXers generalists. And someone who can do visual work. And that was just because everything was tying into IC roles.

And more IC people were kind of, you know, desired basically. Um, granted, they also experienced massive layoffs too. Um, I think in last, Well, this year alone in tech, mostly in us, 80, 000 people lost their jobs. And that’s not just UX, which we tend to focus on what’s painful to us, but it’s everyone. It’s engineers, it’s, it’s product managers.

It’s literally every, every specialty and UX is just part of that. You know, that flow to be laid off. And as you know, I’ve seen a lot of signals, but I feel like it’s, it’s getting better, you know, there’s more IPOs, the markets are recovering. And finally, I saw even this, um, mistake, I think on YouTube the other day, or someone was saying that [00:50:00] for the first time in history, and I’m using a lot of us examples, but I think.

UK follows on a lot of those things because it’s still global economy, but U. S. debt specifically for a first time in like a decade was actually going positive as in it was recovering. Um, cause you know, it’s keeps building up. If you look at the numbers, if you have a calculator daily, you see the numbers increasing by day by minute, almost the debt increasing, but it stopped.

And it was funnily because of April, which was a tax season. And I think, you know, that’s where finally you see all those different kinds of things colliding. But I think there’s bigger market market waves of recovery. And just, again, we see money piling into AI and as such, we almost come back full circle of like what could, could be to come another signal, which was very interesting to see, um, which a few people kind of.

shared and talked about, but I think my take is that there is probably a bit [00:51:00] bigger demand for UX research roles. And, and opening wise, there are still more designers desired for any challenge, but if you compare just the growth rate. between product design and UX research, where it’s still an uptick in UX research, because I think companies realize if all those layoffs that we were a bit too harsh.

Mike: Well, you’ve seen the graph, you know, there’s that website, what is it? Layoffs. org. And it kind of, particularly in research, it was a terrifying kind of drop over the last couple of years. I mean, this is not just six months. This has been going on longer. Yeah. We all hope that that we’ve sort of bottom of the curve has been reached and we’re on the way back up, but, um, 

Vy: yeah, 

Mike: it’s, it’s, it’s, it’s, and particularly when you see experienced people who have been out of work for a while and who are putting kind of ever more plaintive messages on LinkedIn.

It is concerning. I mean, I I’ve worked with a couple of, um, in previous with previous clients in a couple of individuals who were permanent, but perhaps more junior who [00:52:00] would sort of, you know, People I’ve bumped into on the circuit who’d said, what advice can you give me about contracting? And my response is at the moment, don’t even think about it.

If you’ve got a secure quote unquote permanent job, then stay there because the contract market is not a place you want to be in unless you’re leaving. If you’re experienced, it’s not a good place to be, but hopefully those days are ending and we’re on the way up as you say. 

Vy: Yeah, but I guess your, your choice, I guess, in terms of the work has been contracting, right?

But how did you, how did you stay competitive or like, what would be your advice to stay competitive? Because I think contracting is one side of the things, right? Like, and one type of work you could be doing. It’s probably applicable to full timers as well, because again, there’s so much more people out there looking for the job.

So even if you have one or 10 extra roles in UX research. It’s still more people competing, but like how, what would be your advice to say competitive? 

Mike: Well, there’s always that chest, you know, the, the quote, and I’m going to misquote it, but was it Albert Hitchcock was supposed to have said, uh, Albert [00:53:00] Hitchcock, the great film director was supposed to have said to his, to his.

You know, turn up on time, learn your lines and don’t bump into the furniture. And there’s a lot to be said, you know, a life skill in general is don’t be a D I C K excuse my French. Um, you know, and I think in terms of being not, not a pushover, cause you need to kind of challenge people where, when, when, when you have beliefs and when you want to, but I think just being easy to work with, I suppose is what I’m trying to say, but do a good job.

Have an active network that you keep kind of, you know, alive and you keep reaching out to share the knowledge you acquire. I think all of those are skills in terms of being competitive and keeping, and also keep current, you know, keep up to date in the, in the market, keep your skillset, you know, whether it be AI, whether it be methodology, whatever it might be, the challenge is keeping up to date because the world is moving ever faster.

You know, one of the reasons I started my own podcast was, I guess, to to try and share a little bit of the knowledge I’ve got or have the chance for people I’ve worked with who I respected and thought were really good onto the podcast to talk about the work they do. [00:54:00] So it’s that, I suppose, giving back or paying forward, whatever the expression is, to, um, you know, because it’s a really nice discipline, isn’t it?

I mean, so many people I’ve spoke to have said this, that one of the reasons, you know, I suppose, going back to your point about why UX is everyone I met when I was investigating it, I thought they’re just really decent people because, You know, you, you need empathy, you need compassion, you need it. You know, the human centricity by its nature, you need to be a, you can’t be a, you know, so and so and most, but, you know, with very few exceptions, all of the UXs I’ve worked with down the years have just been really, you know, intelligent, curious, hardworking, many or most of them, but at the same time, you know, fundamentally decent people.

And I think that’s one of the lovely things about this discipline. Yeah, I guess it 

Vy: depends too, because I’ve experienced quite, you know, it is clearly a split because there is also people who in particular in startups, let’s say, you know, the environment sometimes gets very sour in certain industries and especially if design is being, or UX is being very [00:55:00] generalist and being positioned to just churn or produce, uh, people get very bogged down and then you get A lot of very spicy takes and negative takes and, and people even say that they wouldn’t advise anyone to get into UX, which to me is very dangerous because, because it doesn’t, it doesn’t just shoot in the foot someone who might have been the superstar UXer in the future, but also shoots that person in the foot cause.

You know, we are all aging and also, you know, you are not going to keep up with the demand and the less of the support you have and the less of the people who are skilled in UX you have, and we have appreciation, the less anyone else is going to appreciate it. Like being a single designer in a company, let’s say, I’ve been single designer in a company.

I know how hard it tends to be when you’re the only one. And if you’re the only one in the market, you’re Or in the industry, it might sound lucrative, but it isn’t. And 

Mike: the [00:56:00] same with like, you know, the first researcher in an organization, that’s a tough place to be because you’re, you’re trying to sell the value at the same time as doing the work.

And I had this experience, you know, at a client recently where a team was brought in to try and kind of upskill them in user centered design. And not only were we trying to deliver, but we were also kind of trying to educate them at the same time. So it’s that, you know, that scene with, have you ever seen the film Wallace and grommet, you know, the cartoon where he’s on top of the train, throwing the track down.

And I’ve used that cliche 70 times, but it’s true. You’re, you’re literally putting the track in front of the train as you’re going along, because you’re like, this is what we’re doing and this is why we’re doing it and this is how it will benefit you and that’s, that’s a tough place to be, and you just need enough sort of critical mass in the organization of decision makers to start saying.

Yeah, we get this and we see the value in this. Um, but that’s not always forthcoming and I guess to your point about sour experiences in, in startups, I don’t want to sound like some naive head in the clouds that everything’s wonderful in UX. I’m certainly, you know, this is, you know, we work in the commercial environment that there are always pressures.

There’s always, you know, politics, there’s always [00:57:00] territoriality. Um, but I think as individual humans, generally most UXs I’ve worked with have tended to be, you know, they’re on the, they’re on the right side of. Things, if I can put it like that, because by nature you need to want to improve the world to do what we do.

Vy: Yeah, absolutely. And, and you, you become so good at identifying problems. That’s where I think that sourness sometimes comes because, you know, the more problems you see for your users and yourself, the, you know, the bigger the head gets, basically, the egos get inflamed and, You know, I’ve been guilty of that so many times, especially in the beginning of my career.

But do you have any advice for, I guess, most junior people? Like, I think some of the audience are going to be those who are going to be, I’ve talked to so many people who are very discouraged right now. And that’s why I’m like looking off, you know, all those competitive bits, but like, what would be your advice for someone who is very new to this?

Just to 

Mike: pick up on that, when you say you’ve spoken to people who are [00:58:00] discouraged, is that people who are in the industry or are trying to get into the industry? I mean, what’s discouraging? It’s both, but 

Vy: I’m trying to almost understand, because I feel like people in the industry are going to figure it out and not to be dismissive of their needs, but I think if you’re already in the industry, at least you have a padded resume and you have some network, maybe not, you know, the most healthy or the most flourished, but you still have a start.

There is so many people who took bootcamps. And got promised to get the job and they are not the same. My, I run the, this, uh, design squad community on discord. That’s one of the largest in UX. It has 14, 000 members and I can guarantee maybe 10, 000 of them are entry level or career changers or juniors, but they’re looking for that bump.

You know, you’re looking for that opportunity where their portfolio of a resume is going to align with someone, um, Who, who would take, but what would be your advice for 

Mike: them? I mean, it depends what [00:59:00] your situation at the moment, you know, it’s, it’s, but I think there are fundamentals it’s key, you know, and this is, again, this is well publicized elsewhere, but it’s things like, you know, keep up to date with developments, you know, whether we talked a lot about AI, whether it be AI or other tooling, you know, I think be aware of, of trends in the, in, in the industry.

So I think, you know, there’s lots of, one, and again, in terms of the niceness of UX is a lot of sharing, a lot of transparency. See. In line with other careers, which by nature tend to be less transparent, perhaps because they have to be. Um, I think there’s a huge amount of content information. There’s websites, there’s blogs, there’s podcasts, there’s all sorts of stuff you can find out online about UX.

And I think that there, there’s some great resources to, to, to, to look at. Um, network furiously, I think, you know, the sort of like you’re talking about Discord community. How do you mean? 

Vy: How do you mean that though? Um, 

Mike: well, whether it be in person, you know, go to events, go to conferences, get yourself known, go to meetups, all of those things, um, get out there and, and, and just start.

And if you can find a mentor, I think that’s a [01:00:00] really good thing. And I know there’s, you know, there are various ADP lists, there’s various organizations or lists that offer, You know, mentoring, I don’t know how, how much it costs. I haven’t really looked into it myself, but, um, if you can find even, even in the organization you’re in, or, you know, adjacent to you, somebody who’s doing a similar role or, you know, a more senior person who you kind of can emulate, can listen to, can learn from, I think there’s a huge amount of value in that.

And just, you know, picking their brains and trying to absorb as much as you can before, you know, you make the next move. So it’s, it’s a variety of things. It’s a kind of knowledge, knowledge, growth, it’s networking. It’s the human side of things. It’s, um, you know, finding people you can learn from and just going out there and trying to do some of this stuff, you know, even, you know, we all have to pay bills, but if you can, if you really are starting from a fresh, it’s that, can you find a project that, uh, that, uh, perhaps a nonprofit wants to do or a local charity or something, they may not be able to pay you for it, but it gets something on your CV.

It gives you experience. It’s something you can talk about in an interview. You know, those are the kinds of things I would suggest. 

Vy: I [01:01:00] agree. It’s a, especially if the last part, cause my, my, I guess, main piece of advice has always been that you kind of have to. You, you have to work on something until you get that work.

Um, because nobody’s just coming out of a crowd and picking you because there’s so many people to be picked out of, you know, it’s again, one opening. And if you look on LinkedIn for any role, a junior or a midweight or senior, there are literally hundreds of applicants, and this is for full time roles and.

For contracting, it’s unspeakable because for contracting, you already have to have enough experience to do that, which I’m sure you would agree. But, 

Mike: and then I think it’s, you know, I do agree with that. And it’s that, you know, what’s your USP, what’s your secret source? What can you bring that others can’t do?

Is it your, I say, no, is it your personality, but is it, you know, is it subject matter expertise? Is it familiarity with a certain tool set? Um, so for example, you know, we talked about my role in government. It wasn’t certainly a long term [01:02:00] objective when I started contracting to become a, you know, user research lead in government, but, but that’s how it’s kind of ended up and fortuitously it’s allowed me to, you know, get other roles and, and, you know, network across government and stuff.

So. You know, I consider myself quite, you know, experienced in that domain. Arguably less so if I was to try and get a research role in, in a bank or a, you know, or a, or a, some other organization. So I guess if you can work out what interests you, uh, you know, what areas of specialism and perhaps you want to, to, to focus on, that’s another good thing to think about.

Vy: There’s so many people who would pick a channel like, oh, I’m gonna, I wanna work on VR, or I’m gonna work on ai. Industry and the type of work is also very, very big one. You mentioned your podcast, right? Like, and you, you have way more episodes than I have. So, so there is like so many, I guess, insights for people to look at, especially who are keen about UX, UX research, but like.

Why did you decide to do that? 

Mike: It’s a, yeah, it’s a good, it’s funny. A number of people have asked me that. And I think I just, it was, so my podcast [01:03:00] is called Understanding Users. So it’s, I host it on PubBean, but it’s available on Spotify. It’s available on Apple. It’s available on, you know, all the usual places.

It’s also on YouTube. And I kind of started it up as a bit of a sort of side hustle, something to, to keep, you know, me interested in parallel with my day job. Uh, you know, it wasn’t a moneymaker. It’s happily derived a little bit of income, you know, it’s certainly not enough to retire on. Um, but it’s something I enjoy doing.

I’ve had the privilege of meeting lots of great guests and I’ve, I’ve been to a number of conferences. So I went to CHI, the big, uh, international kind of user research, the academic conference that’s held around the world. So last year I went to Hamburg, uh, and that was fascinating. Just wandering the halls with my, with my phone.

Talking to researchers, academic researchers and digital experts in, in and around the conference talks about the work they were doing and the, you know, the, the kind of, and obviously AI was on everybody’s tongue even way back then, you know, a year ago. So there’s an episode, a couple of episodes I did there.

I went to New York in May last year to one of the big tech conferences there. And [01:04:00] similarly got talking to a whole bunch of really great people. So, um, yeah, so it’s everything from people I’ve worked with in the early, early episodes, people I’ve had the Privilege of working with and friends of mine on their work, you know, a whole variety of disciplines from, you know, UX design to, to research, to service design, to development, uh, product owners, business owners, um, all sorts of things about their career and their work.

And then, uh, yesterday to conferences as well. And it’s just, you know, there’s no sort of longterm game plan, but I tend to just. Interview people I find interesting and I think would interest the listeners. So, you know, I’d encourage anyone to have a listen and give me some feedback. I’m, I’m on LinkedIn.

There’s a link to my website from the podcast as well, uh, researchable. uk. Uh, and, um, yeah, always keen to get feedback. 

Vy: Awesome. Yeah. I think it’s, it’s a really good place to get that. And I’m the same by the way. Uh, my podcast has been very selfish in a way where I, I kind of started recording videos, chatting with people, but then it.

It wasn’t [01:05:00] official. And then I just said, okay, I’ll make a plunge and share it with the community. Because I think people need to, especially in these shaky, you know, times, they need to understand what people who are experienced think about these things and, you know, kind of deal with that uncertainty socially.

But I really appreciate our chat. And, um, if you don’t mind, I might invite you for round two at some point in the future. 

Mike: I would absolutely love to be, this has been so enjoyable. Thank you so much.


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